Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies. To bridge the gap between the needs of the Data-Intensive Research fields and the current DBMS technologies, we are developing SciQL (pronounced as ‘cycle’), an SQL-based query language for scientific applications with both tables and arrays as first class citizens. It provides a seamless symbiosis of array-, set- and sequence- interpretations. A key innovation is the extension of value-based grouping of SQL:2003 with structural grouping, i.e., fixed-sized and unbounded groups based on explicit relationships between elements positions. This leads to a generalisation of window-based query processing with wide applicability in science domains. In this demo, we show the main features of SciQL using use cases of remote sensing image processing.
SciQL, array query language, array data processing, scientific data processing
Information (theme 2)
An XLDB-2012 Demonstration
Database Architectures

Zhang, Y, & Kersten, M.L. (2012). Scientific Data Processing Using SciQL.